Digital epidemiology of high-frequency search listening trends for the surveillance of subjective well-being during COVID-19 pandemic
Bibliographic record
Abstract
Background: The coronavirus disease (COVID-19) pandemic has led to a dramatic increase in online searches related to psychological distress. Governments worldwide have responded with various measures to mitigate the impact of the virus, influencing public behavior and emotional well-being. This study investigated the relationship between government actions and public reactions in terms of online search behaviors, particularly concerning psychological distress during the pandemic. The primary objective of this study was to analyze how changes in government policies during the COVID-19 pandemic influenced public expressions of psychological distress, as reflected in the volume of related online searches in Kuwait. Method: Utilizing Google Trends data, the study analyzed search frequencies for terms associated with psychological distress such as "anxiety" and "lockdown." The analysis correlated these search trends with government actions using the Oxford COVID-19 Government Response Tracker (OxCGRT). The study period covered March 1, 2020, to October 10, 2020, and involved extensive data collection and analysis using custom software in R programming. Results: There was a significant correlation between the stringency of government-imposed restrictions and the volume of online searches related to psychological distress. Increased searches for "lockdown" coincided with heightened government restrictions and were associated with increased searches for "anxiety," suggesting that policy measures significantly impacted public psychological distress. Conclusion: The study concludes that governmental responses to the COVID-19 pandemic, measured through OxCGRT, have a measurable impact on public psychological distress, as evidenced by online search behaviors. This underscores the importance of considering psychological impacts in policymaking and suggests further research to explore this dynamic comprehensively. Future studies should focus on refining the correlation between specific types of policy measures and different expressions of psychological distress to better inform public health strategies and interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".